In Patent Documents 1 to 5 and so on, the present inventors have proposed an art to detect vibration generated on the body surface of the back in the upper body of a person by a biosignal measurement device and analyze the state of the person. Sound and vibration information arising from motions of the heart and the aorta, which is detected from the back of the upper body of the person, is pressure vibration arising from the motions of the heart and the aorta, and includes information on the systole and diastole of the ventricles, information on vascular wall elasticity which serves as an auxiliary pump for the circulation, and information on reflected waves. That is, the sound and vibration information includes information on a back body surface pulse wave (including an Aortic Pulse Wave (APW)) of around 1 Hz generated on the back surface due to the motions of the heart and the aorta and information on sound conveyed to the back side in accordance with heartbeat (“pseudo heart sound” (in this specification, sound of the heart collected from the back side is referred to as “pseudo heart sound”, in contrast to heart sound which is sound of the heart collected from the chest side). Then, a signal waveform accompanying heart rate variability includes information on neural activities of the sympathetic nervous system and the parasympathetic nervous system, and a signal waveform accompanying aortic oscillation includes information on sympathetic nerve activity.
In Patent Document 1, slide calculation is performed in which a predetermined time width is set in a time-series waveform of back body surface pulse waves (APW) of around 1 Hz extracted from collected biosignals (sound and vibration information), to obtain a frequency gradient time-series waveform, and according to the tendency of its variation, for example, according to whether its amplitude is on the decrease or the increase, a biological state is estimated. It is also disclosed that, by frequency analysis of the biosignal, power spectra of respective frequencies corresponding to predetermined signals such as a function regulation signal, a fatigue reception signal, and an activity regulation signal belonging to a ULF band (ultra low-frequency band) to a VLF band (very low-frequency band) are obtained, and the state of a person is judged from time-series variations of the respective power spectra. Since the fatigue reception signal indicates a progress degree of fatigue in a normal active state, additionally comparing predominant degrees of the power spectra of the function regulation signal and the activity regulation signal makes it possible to judge the state of a person (a sympathetic nerve predominant state, a parasympathetic nerve predominant state, or the like). It is further disclosed that, with the total value of the power spectra of frequency components corresponding to these three signals being set as 100, time-series distribution ratios of the respective frequency components are obtained, and the state of a person is judged using time-series variations of the distribution ratios.
As a method of quantifying a biological state, Patent Document 2 proposes an art to represent the biological state as a physical condition map and a sensation map. To create them, the above-described APW is frequency-analyzed, an analyzed waveform in each target analysis section is displayed on log-log axes, the analyzed waveform is classified into a low-frequency band, an intermediate-frequency band, or a high-frequency band, and according to a gradient of the classified analyzed waveform and the shape of the whole analyzed waveform, the analyzed waveform is scored based on a predetermined criterion, and the results are plotted on coordinate axes. The physical condition map shows the control state of the autonomic nervous system from a viewpoint of the balance between the sympathetic nerve and the parasympathetic nerve, and in the sensation map, the state of a change of heart rate variability is superimposed on the physical condition map.
Patent Documents 3 to 5 disclose a means for judging a homeostasis function level. For the judgment, the means for judging the homeostasis function level uses at least one or more of plus/minus of a differentiated waveform of a frequency gradient time-series waveform, plus/minus of an integrated waveform obtained by integrating the frequency gradient time-series waveform, absolute values of frequency gradient time-series waveforms obtained by absolute value processing of a frequency gradient time-series waveform obtained by a zero-cross method and a frequency gradient time-series waveform obtained by a peak detection method, and so on. By using the combination of these, it is obtained on which level the homeostasis function is. For example, the level can be set such that, when the frequency gradient and the integrated value are used and they are predetermined values or more, it is judged that “the homeostasis function level is 1,” or when the differential value is equal to or less than a predetermined value and “peak is predominant” out of the two absolute values, it is judged that “the homeostasis function level is 4.” The combination of these, a threshold value for the judgment, and so on are determined based on the results of statistical processing of data of many subjects.
Non-patent Document 1 discloses an art to obtain, regarding finger plethysmogram information, a frequency gradient time-series waveform of a power value reflecting information on the sympathetic nerve, and plot integrated values resulting from absolute value processing of this, in a time-series manner as a fatigue degree to depict a fatigue curve, from which muscle fatigue is obtained. Non-patent Document 2 discloses an art to arithmetically process biosignals which are obtained from the back of a person using an air pack sensor, to depict a fatigue curve by a similar method and grasp muscle fatigue. That is, it is possible to grasp the state of the muscle fatigue by using a frequency gradient time-series waveform (a frequency gradient time-series waveform by a zero-cross method in the case of APW) of a power value reflecting information on the sympathetic nerve.